Data-Analysis

🔢 NumPy Masterclass and Practice Sheet From Zero to Confident - A Beginner’s Guide to Numerical Computing in Python Who is this for? You know Python basics - lists, loops, functions - and you want to level up into data science. NumPy is your first real step. Everything in data science (Pandas, Scikit-learn, TensorFlow) is built on top of it. 📌 Table of Contents What is NumPy and Why Should You Care? Installing and Importing NumPy Creating Arrays Array Attributes Indexing and Slicing Array Operations - Math Made Easy Universal Functions (ufuncs) Aggregation Functions Reshaping and Transposing Stacking and Splitting Arrays Boolean Masking and Fancy Indexing Broadcasting Working with Random Numbers Linear Algebra with NumPy Sorting and Searching Copies vs Views Saving and Loading Arrays Practice Questions What We Covered + What’s Next 1. What is NumPy and Why Should You Care? Python lists are flexible and easy to use - but they are slow when it comes to heavy number crunching. If you have a million exam scores and want to find the average, a Python loop will take noticeably longer than it should. ...

🐼 Pandas Masterclass From Raw Data to Real Insights - A Beginner’s Guide to Data Analysis in Python Who is this for? You’ve done the NumPy Masterclass (or know the basics of Python and arrays). Now you want to work with real tabular data - rows, columns, missing values, merges, and group-level summaries. That’s exactly what Pandas is for. 📌 Table of Contents What is Pandas and Why Do You Need It? Installing and Importing Pandas The Two Core Data Structures Creating DataFrames Reading and Writing Data Exploring a DataFrame Selecting Data - loc, iloc, and Column Access Filtering Rows Adding, Modifying, and Dropping Columns Sorting Data Handling Missing Data String Operations Working with Dates and Times apply(), map(), and applymap() GroupBy - Aggregation and Split-Apply-Combine Merging and Joining DataFrames Pivot Tables and Crosstabs Reshaping - melt() and stack()/unstack() MultiIndex DataFrames Performance Tips Practice Questions What We Covered + What’s Next 1. What is Pandas and Why Do You Need It? NumPy arrays are powerful, but they have one limitation: every column must be the same data type, and there are no column names or row labels. ...

NumPy Masterclass - From Basics to Advanced Prerequisites: Python basics, lists, loops, functions Install: pip install numpy Import convention: import numpy as np Table of Contents What is NumPy and Why Use It? Creating Arrays Array Properties Indexing and Slicing Modifying Arrays Reshaping and Stacking Math Operations and Broadcasting Universal Functions (ufuncs) Aggregations and Statistics Sorting and Searching Boolean Operations and Masking Random Number Generation Linear Algebra Working with Files Performance Tips and Best Practices 1. What is NumPy and Why Use It? NumPy (Numerical Python) is the foundation of the entire Python data science stack. Pandas, Matplotlib, Scikit-learn, and TensorFlow all build on top of it. ...

Pandas Masterclass - From Basics to Advanced Prerequisites: Python basics, NumPy fundamentals Install: pip install pandas openpyxl xlrd Import convention: import pandas as pd Table of Contents What is Pandas and Why Use It? Series - The 1D Structure DataFrame - The 2D Structure Loading and Saving Data Exploring Your Data Selecting and Filtering Adding and Modifying Columns Sorting Cleaning Data - Handling Missing Values Cleaning Data - Duplicates and Types String Operations Datetime Operations Groupby and Aggregation Merging, Joining and Concatenating Pivot Tables and Reshaping Apply, Map and Lambda Functions Plotting with Pandas Performance Tips 1. What is Pandas and Why Use It? Pandas provides two core data structures - Series (1D) and DataFrame (2D) - that make working with labeled, tabular data as natural as working with a spreadsheet, but with the full power of Python. ...

Lesson 2 - Filtering & Querying Data Theory + Practice | ShopDB - Indian E-commerce Dataset Before You Start Run setup.sql in pgAdmin to create and populate ShopDB. Verify you see 5 tables with the correct row counts. Then work through this file top to bottom. What you’ll learn in this lesson: Concept What it does DISTINCT Remove duplicate values from results IN / NOT IN Match against a list of values BETWEEN Filter within a range LIKE / ILIKE Pattern matching on text IS NULL / IS NOT NULL Handle missing values CASE WHEN Conditional logic inside a query Date functions Filter and extract parts of dates COALESCE Replace NULL with a fallback value All queries in this lesson run on ShopDB. The dataset has: ...

Lesson 3 - Aggregations & Grouping Theory + Practice | CricketDB + ShopDB Before You Start This lesson uses both databases you have already built: CricketDB - players, matches, performances (Lesson 2.1) ShopDB - customers, products, orders, order_items (Lesson 2.2) Run the setup_check.sql from this lesson folder to confirm both are ready. Each section tells you which database to connect to in pgAdmin. What you will learn in this lesson: ...

Lesson 2.7 - Indexes, Constraints & Schema Design Theory + Practice | All Three Databases Before You Start This lesson uses all three databases. Each section tells you which one to connect to. Quick check - run this in each database before starting: -- In cricketdb: SELECT COUNT(*) FROM performances; -- expect 30 -- In shopdb: SELECT COUNT(*) FROM order_items; -- expect 30+ -- In streamdb: SELECT COUNT(*) FROM watch_history; -- expect 50 What you will learn: ...

SQL Lecture 01 - Introduction to SQL & Databases PostgreSQL | Beginner Reference Sheet | Use this during class 🐘 Table of Contents What is a Database? Types of Databases What is SQL? How SQL Commands Are Grouped Creating a Table - Full Breakdown Data Types in PostgreSQL Constraints - Rules for Your Data Primary Key & Foreign Key Common SQL Commands with Examples What Happens When You Violate a Constraint? Practice Table & 10 Beginner Questions 1. What is a Database? A database is an organised collection of data stored so it can be easily accessed, managed, and updated. ...

Lesson 4 - JOINs & Relationships Theory + Practice | ShopDB Before You Start This lesson uses ShopDB exclusively. Connect to shopdb in pgAdmin before running any query. Run this to confirm your data is ready: SELECT 'customers' AS t, COUNT(*) FROM customers UNION ALL SELECT 'products', COUNT(*) FROM products UNION ALL SELECT 'orders', COUNT(*) FROM orders UNION ALL SELECT 'order_items',COUNT(*) FROM order_items; -- Expected: 20, 25, 15, 30 What you will learn: ...